The present disclosure relates to a method for edge determination of a measurement object in optical metrology. The present disclosure furthermore relates to an optical coordinate-measuring machine and to a computer program product for executing the herein presented method.
Coordinate-measuring machines, as are known for example from DE 10 2012 103 554 A1, serve for checking workpieces, for example as part of quality assurance, or for determining the geometry of a workpiece completely as part of what is known as “reverse engineering.” Moreover, diverse further application possibilities are conceivable.
In coordinate-measuring machines, different types of sensors may be used to capture the workpiece to be measured. By way of example, sensors that measure in tactile fashion are known in this respect, as are sold by the applicant under the name “VAST XT” or “VAST XXT”. Here, the surface of the workpiece to be measured is scanned with a stylus, the coordinates of said stylus in the measurement space being known at all times. Such a stylus may also be moved along the surface of a workpiece in a manner such that a multiplicity of measurement points can be captured at set or known time intervals during such a measurement process as part of what is known as a “scanning method”.
It is moreover known to use optical sensors that facilitate contactless capturing of the coordinates of a workpiece. The present disclosure relates to such a coordinate-measuring machine comprising an optical sensor and to an associated method for optical measurement. One example of an optical sensor is the optical sensor sold by the applicant under the product designation “ViScan”. An optical sensor of this type can be used in various types of measurement setups or coordinate-measuring machines. Examples of such coordinate-measuring machines are the products “O-SELECT” and “O-INSPECT”, which are sold by the applicant.
For an exact measurement, it is mandatory in optical coordinate-measuring machines to provide corresponding illumination of the workpiece to be measured. In addition to what is known as transmitted-light illumination, where the light source is situated, relative to the optical sensor, behind the workpiece, what is known as reflected-light illumination can be alternatively used in order to illuminate the workpiece or the measurement object on its top side, which faces the optical sensor. Illumination that is adapted exactly to the measurement object is of immense importance, in particular because it is possible hereby to improve the bright-to-dark contrast that is necessary in the optical detection of the measurement object. Specifically, during said optical measurement of the measurement object, the measurement object is imaged onto the optical sensor, i.e. a 2D projection of the measurement object onto the sensor plane is produced.
During transmitted-light illumination, regions that are not covered by the measurement object appear bright on the optical sensor. Conversely, regions which are covered by the measurement object appear dark on the optical sensor.
During reflected-light illumination, in particular during bright-field reflected-light illumination, regions of the measurement object that reflect light that is incident thereon appear as bright regions, and regions that do not reflect any light appear as dark regions.
In order to be able to capture the spatial coordinates (2D or 3D coordinates) of the measurement object, first the edges or the position of the edges of the measurement object must be determined. The image data captured by the optical sensor is preferably one or more greyscale image(s). The edges of the measurement object that are to be evaluated for metrological purposes are, for physical reasons, not imaged onto the optical sensor as a binary jump between bright and dark, but as a greyscale profile between bright and dark. The width of this profile is influenced by various factors, such as for example the orientation of the measurement object in the focus plane or the quality/NA of the measurement lens.
The metrological challenge is now to determine the actual position of one or more edges of the measurement object from the image data captured by the optical sensor. More specifically, the challenge is to suitably interpret the greyscale profile produced by the edges of the measurement object in the image data or to apply the criterion at which the edge orientation determined from the greyscale profile corresponds to the physical edge orientation at the measurement object. Fully automated or partially automated, software-based evaluation methods are typically chosen for interpreting the image data and determining the edge. Known edge detection algorithms are, for example, Canny algorithm and Laplacian filter. Other known edge operators are Sobel operator, Scharr operator, Prewitt operator and Roberts operator.
However, it has been found that the above-described type of image evaluation and edge detection can lead to systematic errors. Until now, this type of systematic error has typically been of minor relevance. However, due to more recent measurement methods and an increasing demand for measurement accuracy, this type of measurement deviation increasingly gains in importance. Until now, it has however not yet been possible to find a suitable and cost-effective way of avoiding this type of systematic measurement error.